论文标题
Impash：用于结直肠癌组织分类的新型域抗性抗性表示
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
论文作者
论文摘要
组织病理学图像的出现取决于组织类型，染色和数字化过程。这些因来源而异，是域转移问题的潜在原因。由于这个问题，尽管深度学习模型在计算病理学中取得了巨大的成功，但是在我们将其应用于另一个领域时，在特定领域进行训练的模型仍可能在次优。为了克服这一点，我们提出了一种称为PatchShuffling的新增强，以及一个新颖的自我保护的对比学习框架，名为Impash，以预训练深度学习模型。使用这些，我们获得了一个RESNET50编码器，该编码器可以提取对域移位抗性的图像表示。我们通过使用其他域将代理技术进行了将其派生的表示与基于其他域将来获得的表示形式进行了比较。我们表明，所提出的方法的表现优于其他传统的组织学领域适应和最先进的自我监管学习方法。代码可在以下网址提供：https：//github.com/trinhvg/impash。
The appearance of histopathology images depends on tissue type, staining and digitization procedure. These vary from source to source and are the potential causes for domain-shift problems. Owing to this problem, despite the great success of deep learning models in computational pathology, a model trained on a specific domain may still perform sub-optimally when we apply them to another domain. To overcome this, we propose a new augmentation called PatchShuffling and a novel self-supervised contrastive learning framework named IMPaSh for pre-training deep learning models. Using these, we obtained a ResNet50 encoder that can extract image representation resistant to domain-shift. We compared our derived representation against those acquired based on other domain-generalization techniques by using them for the cross-domain classification of colorectal tissue images. We show that the proposed method outperforms other traditional histology domain-adaptation and state-of-the-art self-supervised learning methods. Code is available at: https://github.com/trinhvg/IMPash .
